Advancing brain network models to reconcile functional neuroimaging and clinical research
Functional magnetic resonance imaging (fMRI) captures information on brain function beyond the anatomical alterations that are traditionally visually examined by neuroradiologists. However, the fMRI signals are complex in addition to being noisy, so fMRI still faces limitations for clinical applicat...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | NeuroImage: Clinical |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158222003278 |
| _version_ | 1811307425571012608 |
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| author | Xenia Kobeleva Gaël Varoquaux Alain Dagher Mohit Adhikari Christian Grefkes Matthieu Gilson |
| author_facet | Xenia Kobeleva Gaël Varoquaux Alain Dagher Mohit Adhikari Christian Grefkes Matthieu Gilson |
| author_sort | Xenia Kobeleva |
| collection | DOAJ |
| description | Functional magnetic resonance imaging (fMRI) captures information on brain function beyond the anatomical alterations that are traditionally visually examined by neuroradiologists. However, the fMRI signals are complex in addition to being noisy, so fMRI still faces limitations for clinical applications. Here we review methods that have been proposed as potential solutions so far, namely statistical, biophysical and decoding models, with their strengths and weaknesses. We especially evaluate the ability of these models to directly predict clinical variables from their parameters (predictability) and to extract clinically relevant information regarding biological mechanisms and relevant features for classification and prediction (interpretability). We then provide guidelines for useful applications and pitfalls of such fMRI-based models in a clinical research context, looking beyond the current state of the art. In particular, we argue that the clinical relevance of fMRI calls for a new generation of models for fMRI data, which combine the strengths of both biophysical and decoding models. This leads to reliable and biologically meaningful model parameters, which thus fulfills the need for simultaneous interpretability and predictability. In our view, this synergy is fundamental for the discovery of new pharmacological and interventional targets, as well as the use of models as biomarkers in neurology and psychiatry. |
| first_indexed | 2024-04-13T09:04:09Z |
| format | Article |
| id | doaj.art-43eb83e90c8e4ed6bd1393b5e984d24b |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-04-13T09:04:09Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-43eb83e90c8e4ed6bd1393b5e984d24b2022-12-22T02:53:02ZengElsevierNeuroImage: Clinical2213-15822022-01-0136103262Advancing brain network models to reconcile functional neuroimaging and clinical researchXenia Kobeleva0Gaël Varoquaux1Alain Dagher2Mohit Adhikari3Christian Grefkes4Matthieu Gilson5Department of Neurology, University of Bonn, Bonn, Germany; German Center for Neurodegenerative Diseases (DZNE) Bonn, Bonn, GermanyINRIA Saclay, Paris, FranceMontreal Neurological Institute, McGill University, Montréal, CanadaBio-imaging Lab, University of Antwerp, Antwerp, BelgiumDepartment of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Institute of Neuroscience and Medicine (INM-1, INM-3), Research Centre Juelich, Juelich, Germany; Department of Neurology, Goethe University Frankfurt, Frankfurt, GermanyInstitute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany; Center for Brain and Cognition, Department of Information and Telecommunication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France; Corresponding author.Functional magnetic resonance imaging (fMRI) captures information on brain function beyond the anatomical alterations that are traditionally visually examined by neuroradiologists. However, the fMRI signals are complex in addition to being noisy, so fMRI still faces limitations for clinical applications. Here we review methods that have been proposed as potential solutions so far, namely statistical, biophysical and decoding models, with their strengths and weaknesses. We especially evaluate the ability of these models to directly predict clinical variables from their parameters (predictability) and to extract clinically relevant information regarding biological mechanisms and relevant features for classification and prediction (interpretability). We then provide guidelines for useful applications and pitfalls of such fMRI-based models in a clinical research context, looking beyond the current state of the art. In particular, we argue that the clinical relevance of fMRI calls for a new generation of models for fMRI data, which combine the strengths of both biophysical and decoding models. This leads to reliable and biologically meaningful model parameters, which thus fulfills the need for simultaneous interpretability and predictability. In our view, this synergy is fundamental for the discovery of new pharmacological and interventional targets, as well as the use of models as biomarkers in neurology and psychiatry.http://www.sciencedirect.com/science/article/pii/S2213158222003278Whole-brain modelfMRI dataDiagnosisBiomarkerModel interpretationNeuropathologies |
| spellingShingle | Xenia Kobeleva Gaël Varoquaux Alain Dagher Mohit Adhikari Christian Grefkes Matthieu Gilson Advancing brain network models to reconcile functional neuroimaging and clinical research NeuroImage: Clinical Whole-brain model fMRI data Diagnosis Biomarker Model interpretation Neuropathologies |
| title | Advancing brain network models to reconcile functional neuroimaging and clinical research |
| title_full | Advancing brain network models to reconcile functional neuroimaging and clinical research |
| title_fullStr | Advancing brain network models to reconcile functional neuroimaging and clinical research |
| title_full_unstemmed | Advancing brain network models to reconcile functional neuroimaging and clinical research |
| title_short | Advancing brain network models to reconcile functional neuroimaging and clinical research |
| title_sort | advancing brain network models to reconcile functional neuroimaging and clinical research |
| topic | Whole-brain model fMRI data Diagnosis Biomarker Model interpretation Neuropathologies |
| url | http://www.sciencedirect.com/science/article/pii/S2213158222003278 |
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